Many security systems today seek to automatically detect threats such as guns, knives, and explosives using an x-ray imaging system. In particular, the use of dual energy x-ray imaging is often desirable because it allows for the determination of chemical properties of the objects in the scan, such as atomic number and mass density. Dual-energy x-ray imaging is typically performed using a full 3D reconstruction (e.g., a CT image) of the scanned object, and entails obtaining a very large number of scans of the static object. Using the large number of scans, the system is able to identify physical properties of the material at each point in space.
However, systems performing a full 3D reconstruction are generally too slow and expensive to be used for high-throughput applications. Thus, there is a need for methods that operate with a smaller number of views. Some known methods perform a coarse 3D reconstruction using an algebraic reconstruction technique with a limited number of views. One method uses three views to match outlines of objects between views and estimate a rough 3D shape of the objects. Another method uses two views to produce a stereoscopic x-ray image for viewing. These methods rely on knowing the precise angles of the views, and having the scanned objects remaining static between these views.
Fusing the information from multiple views allows for a better interpretation of the images than considering each angle in isolation. For the fusing to be performed effectively, a method for extracting data from multiple views while relaxing the strict requirements of known viewpoint or stationary rigid objects is desired. In the case of natural images, stereo vision methods have been developed that allow for less restricted and even unknown viewing angles (possibly solving for the viewing angle in the process) or for handling objects that move between images acquired from the same view. Typically, these methods are used to estimate the 3D structure of the scene in the form of a depth map.
Generally, stereo methods are more flexible than current x-ray reconstruction techniques, since they imply weaker assumptions on how images are acquired. However, there are significant obstacles to applying existing stereo methods from computer vision directly to x-ray images. First, x-ray systems produce transmission images obtained by transmission of waves that are attenuated as they pass through objects. Hence, unlike in standard vision obtained by light reflection, where an opaque object occludes all the objects that are behind it, objects in an x-ray scan look as though they are semi-transparent and overlapping. The attenuation captured in an x-ray image is additive: the attenuation value at every pixel of the attenuation image is a linear combination of the attenuation from multiple “overlapping” objects that the wave passed through on its way from the source to the sensor. A second obstacle in applying existing stereo methods to the current problem is that many of the applicable existing methods do not yield accurate results when the angle between views is unknown and/or the objects move between views.
The general concept of capturing multiple images with a small angle offset for the purposes of stereoscopic imaging has been disclosed. However, these disclosures describe a physical method of obtaining the images without any reference to how the images would be analyzed. A method and apparatus that allows analysis of overlapping objects in an image in an accurate yet cost-effective manner is desired.